Ontology Summit 2015 : Track A Session - Ontology Integration in the Internet of Things - Thu 2015-02-05,
http://ontolog-02.cim3.net/wiki/ConferenceCall_2015_02_05
invited talk at iPHEM16, Innovation in Pre-hospital Emergency Medicine, Kent Surrey and Sussex Air Ambulance Trust, July 2016, Brighton, United Kingdom
A Unified Semantic Engine for Internet of Things and Smart Cities: From Senso...Amélie Gyrard
A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor Data to End-Users Applications
The 8th IEEE International Conference on Internet of Things (iThings 2015), 11-13 December 2015, Sydney, Australia
Amelie Gyrard, Martin Serrano
invited talk at iPHEM16, Innovation in Pre-hospital Emergency Medicine, Kent Surrey and Sussex Air Ambulance Trust, July 2016, Brighton, United Kingdom
A Unified Semantic Engine for Internet of Things and Smart Cities: From Senso...Amélie Gyrard
A Unified Semantic Engine for Internet of Things and Smart Cities: From Sensor Data to End-Users Applications
The 8th IEEE International Conference on Internet of Things (iThings 2015), 11-13 December 2015, Sydney, Australia
Amelie Gyrard, Martin Serrano
This presentation given in the un-conference on technology and art is a first glance into new research on taking event-driven technology to create a new type of literature.
The Internet of Things, Ambient Intelligence, and the Move Towards Intelligen...George Vanecek
With the successful adoption of cloud-based services and the increasing capabilities of smart connected/wireless devices, the software and consumer electronics industries are turning towards innovating solutions within the Internet-of-Things (IoT) to offer consumers (and enterprises) smart solutions that take the dynamics of the real-world into consideration.
The vision is to bring the awareness of what happens in the real-world, how people live and how smart devices operate in the real world into the view and control of the digital world. Here the digital world is the totality of the Internet, the Web, and the private and public cloud services.
In this session, we will look at key technical trends and their increasing interdependency in the areas of real-world Sensing, Perception, Machine Learning, Context-awareness, dynamic Trust Determination, Semantic Web and Artificial Intelligence which are now enabling ambient intelligence and driving the emergence of Intelligence Systems within the Internet of Things. We will also look at the challenges that such interdependencies expose, and the opportunities that their solutions offer to the industry.
Meetup #3 - Cyber-physical view of the Internet of EverythingFrancesco Rago
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Get Cloud Resources to the IoT Edge with Fog ComputingBiren Gandhi
Fog Computing as a foundational architectural concept for Internet of Things (IoT) and Internet of Everything (IoE).
Embedded devices in the IoT are hampered by the compute, storage, and service limitations of living life on the edge. As IoT edge devices comprise broader sensor networks for industrial automation, transportation, and other safety critical applications, their high uptime requirements are nonnegotiable and service latencies must be kept within realtime or near real time parameters. However, the size, weight, power, and cost constraints of edge platforms also inhibit the ondevice resources available for executing such functions. In this session, Gandhi will introduce Fog Computing, a new paradigm for the IoT that extends compute, storage, and application resources from the cloud to the network edge. Beyond the interplay between Fog and Cloud, Gandhi will show how Fog services can be leveraged across a range of heterogeneous platforms—from end user devices and access points to edge routers and switches—through software technology that facilitates the collection, storage, analysis, and fusion of data to drive success in your next IoT device deployment.
Implementing this concept is not an easy task by any measure for many reasons including the complex nature of the different components of the ecosystem of IoT. To understand the gravity of this task, we will explain all the five components of IoT Implementation
Research Inventy : International Journal of Engineering and Scienceinventy
Research Inventy : International Journal of Engineering and Science is published by the group of young academic and industrial researchers with 12 Issues per year. It is an online as well as print version open access journal that provides rapid publication (monthly) of articles in all areas of the subject such as: civil, mechanical, chemical, electronic and computer engineering as well as production and information technology. The Journal welcomes the submission of manuscripts that meet the general criteria of significance and scientific excellence. Papers will be published by rapid process within 20 days after acceptance and peer review process takes only 7 days. All articles published in Research Inventy will be peer-reviewed.
SEMANTIC TECHNIQUES FOR IOT DATA AND SERVICE MANAGEMENT: ONTOSMART SYSTEMijwmn
In 2020 more than50 billions devices will be connected over the Internet. Every device will be connected to
anything, anyone, anytime and anywhere in the world of Internet of Thing or IoT. This network will
generate tremendous unstructured or semi structured data that should be shared between different
devices/machines for advanced and automated service delivery in the benefits of the user’s daily life. Thus,
mechanisms for data interoperability and automatic service discovery and delivery should be offered.
Although many approaches have been suggested in the state of art, none of these researches provide a fully
interoperable, light, flexible and modular Sensing/Actuating as service architecture. Therefore, this paper
introduces a new Semantic Multi Agent architecture named OntoSmart for IoT data and service
management through service oriented paradigm. It proposes sensors/actuators and scenarios independent
flexible context aware and distributed architecture for IoT systems, in particular smart home systems.
SEMANTIC TECHNIQUES FOR IOT DATA AND SERVICE MANAGEMENT: ONTOSMART SYSTEMijwmn
In 2020 more than50 billions devices will be connected over the Internet. Every device will be connected to
anything, anyone, anytime and anywhere in the world of Internet of Thing or IoT. This network will
generate tremendous unstructured or semi structured data that should be shared between different
devices/machines for advanced and automated service delivery in the benefits of the user’s daily life. Thus,
mechanisms for data interoperability and automatic service discovery and delivery should be offered.
Although many approaches have been suggested in the state of art, none of these researches provide a fully
interoperable, light, flexible and modular Sensing/Actuating as service architecture. Therefore, this paper
introduces a new Semantic Multi Agent architecture named OntoSmart for IoT data and service
management through service oriented paradigm. It proposes sensors/actuators and scenarios independent
flexible context aware and distributed architecture for IoT systems, in particular smart home systems.
SEMANTIC TECHNIQUES FOR IOT DATA AND SERVICE MANAGEMENT: ONTOSMART SYSTEMijwmn
In 2020 more than50 billions devices will be connected over the Internet. Every device will be connected to
anything, anyone, anytime and anywhere in the world of Internet of Thing or IoT. This network will
generate tremendous unstructured or semi structured data that should be shared between different
devices/machines for advanced and automated service delivery in the benefits of the user’s daily life. Thus,
mechanisms for data interoperability and automatic service discovery and delivery should be offered.
Although many approaches have been suggested in the state of art, none of these researches provide a fully
interoperable, light, flexible and modular Sensing/Actuating as service architecture. Therefore, this paper
introduces a new Semantic Multi Agent architecture named OntoSmart for IoT data and service
management through service oriented paradigm. It proposes sensors/actuators and scenarios independent
flexible context aware and distributed architecture for IoT systems, in particular smart home systems.
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for beginners, providing thorough training in areas such as SEO, digital communication marketing, and PPC training in Noida. After finishing the program, students receive the certifications recognised by top different universitie, setting a strong foundation for a successful career in digital marketing.
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For more detailed information on delivering micro-credentials in TVET, visit this https://tvettrainer.com/delivering-micro-credentials-in-tvet/
Executive Directors Chat Leveraging AI for Diversity, Equity, and InclusionTechSoup
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1. Dynamic Semantics for the
Internet of Things
1
Payam Barnaghi
Institute for Communication Systems (ICS)
University of Surrey
Guildford, United Kingdom
3. Data in the IoT
− Data is collected by sensory devices and also crowd
sensing sources.
− It is time and location dependent.
− It can be noisy and the quality can vary.
− It is often continuous - streaming data.
− There are other important issues such as:
− Device/network management
− Actuation and feedback (command and control)
− Service and entity descriptions are also important.
4. Internet of Things: The story so far
RFID based
solutions
Wireless Sensor and
Actuator networks
, solutions for
communication
technologies, energy
efficiency, routing, …
Smart Devices/
Web-enabled
Apps/Services, initial
products,
vertical applications, early
concepts and demos, …
Motion sensor
Motion sensor
ECG sensor
Physical-Cyber-Social
Systems, Linked-data,
semantics, M2M,
More products, more
heterogeneity,
solutions for control and
monitoring, …
Future: Cloud, Big (IoT) Data
Analytics, Interoperability,
Enhanced Cellular/Wireless Com.
for IoT, Real-world operational
use-cases and Industry and B2B
services/applications,
more Standards…
5. Scale of the problem
5
Things Data
Devices
2.5 quintillion
bytes per day
Billions and
Billions of
them…
Estimated 50
Billion by 2020
7. Human Brain and (Sensory) Big Data
− Collecting the data is done by human
senses but encoding and retrieving it is a
bigger challenge.
− The two key properties of the human brain
and its design are Richness and
Associative Access*.
− Associative access enables us to access
our thoughts in different ways by semantic
or perceptual associations.
− Brian can process these data and provide
actionable-knowledge.
7
Image source: Wikipedia
* The organised Mind, Daniel J. Levitin, Penguin Books.
8. IoT and and (Sensory) Big Data
− Collecting data is not the most difficult challenge
(of course we still need better devices, more
energy efficient devices/way of collecting data,
intelligent networks and better telecom)
− The biggest challenge is to organise and
access/retrieve data more efficiently and by
using different (high-level) associations.
− We need to integrate different sources and
process/analyse them to extract actionable-
information from the raw data.
− Semantic technologies and rich metadata seem
to be the way forward.
8
9. 9
But why don’t we still have fully
integrated semantic solutions in the
IoT?
10. 10
Some good existing models:
SSN Ontology
Ontology Link: http://www.w3.org/2005/Incubator/ssn/ssnx/ssn
M. Compton et al, "The SSN Ontology of the W3C Semantic Sensor Network Incubator Group", Journal of Web Semantics, 2012.
12. 12
We have good models and description
frameworks;
The problem is that having good
models and developing ontologies is
not enough.
13. 13
Semantic descriptions are intermediary
solutions, not the end product.
They should be transparent to the end-
user and probably to the data producer
as well.
15. Publishing Semantic annotations
− We need a model (ontology) – this is often the easy part
for a single application.
− Interoperability between the models is a big issue.
− Express-ability vs Complexity is a challenge.
− How and where to add the semantics
− Where to publish and store them
− Semantic descriptions for data, streams, devices
(resources) and entities that are represented by the
devices, and description of the services.
15
17. Hyper/CAT
17
Source: Toby Jaffey, HyperCat Consortium, http://www.hypercat.io/standard.html
- Servers provide catalogues of resources to
clients.
- A catalogue is an array of URIs.
- Each resource in the catalogue is annotated
with metadata (RDF-like triples).
19. 19
Complex models are (sometimes) good
for publishing research papers….
But they are often difficult to
implement and use in real world
products.
20. What happens afterwards is more important
− How to index and query the annotated data
− How to make the publication suitable for constrained
environments and/or allow them to scale
− How to query them (considering the fact that here we are
dealing with live data and often reducing the processing
time and latency is crucial)
− Linking to other sources
20
21. The IoT is a dynamic, online and rapidly
changing world
21
isPartOf
Annotation for the (Semantic) Web
Annotation for the IoT
Image sources: ABC Australia and 2dolphins.com
24. 24
We should accept the fact that
sometimes we do not need (full)
semantic descriptions.
Think of the applications and use-cases
before starting to annotate the data.
25. An example: a discovery
method in the IoT
time
location
type
Query formulating
[#location | #type | time][#location | #type | time]
Discovery ID
Discovery/
DHT Server
Data repository
(archived data)
#location
#type
#location
#type
#location
#type
Data hypercube
Gateway
Core network
Network Connection
Logical Connection
Data
26. An example: a discovery method in the IoT
26
S. A. Hoseinitabatabaei, P. Barnaghi, C. Wang, R. Tafazolli, L. Dong, "A Distributed Data Discovery Mechanism for the Internet of Things",
2014.
27. An example: a discovery method in the IoT
27
S. A. Hoseinitabatabaei, P. Barnaghi, C. Wang, R. Tafazolli, L. Dong, "A Distributed Data Discovery Mechanism for the Internet of Things",
2014.
28. 28
Semantic descriptions can be fairly
static on the Web;
In the IoT, the meaning of data and
the annotations can change over
time/space…
31. Dynamic annotations for data in the
process chain
31S. Kolozali et al, A Knowledge-based Approach for Real-Time IoT Data Stream Annotation and Processing", iThings 2014, 2014.
32. Overall, we need semantic technologies
in the IoT and these play a key role in
providing interoperability.
33. However, we should design and use
the semantics carefully and
consider the constraints and
dynamicity of the IoT environments.
35. #1: Design for large-scale and provide tools and
APIs.
#2: Think of who will use the semantics and how
when you design your models.
#3: Provide means to update and change the
semantic annotations.
35
36. #4: Create tools for validation and interoperability
testing.
#5: Create taxonomies and vocabularies.
#6: Of course you can always create a better
model, but try to re-use existing ones as much as
you can.
36
37. #7: Link your data and descriptions to other
existing resources.
#8: Define rules and/or best practices for providing
the values for each attribute.
#9: Remember the widely used semantic
descriptions on the Web are simple ones like
FOAF.
37
38. #10: Semantics are only one part of the solution
and often not the end-product so the focus of the
design should be on creating effective methods,
tools and APIs to handle and process the
semantics.
Query methods, machine learning, reasoning and
data analysis techniques and methods should be
able to effectively use these semantics.
38
In Conclusion